28th EuroAD Workshop

Session

Keynotes

Dec 8, 2025, 2:15 PM

31/3-004 - IT Amphitheatre (CERN)

Conveners

Keynotes

• Vassil Vasilev (Princeton University (US))

Keynotes

• Vassil Vasilev (Princeton University (US))

Presentation materials

Introduction.pdf

Introduction.pptx

30. Automatically Differentiating Parametric Discontinuities

Prof. Tzu-Mao Li (UCSD)

12/8/25, 2:15 PM

I will talk about our recent works on automatic differentiation algorithms for differentiating low-dimensional integrals of discontinuous functions, which are common in computer graphics, vision, and machine learning applications. Our method makes minimal assumptions on the program structure and does not require specialized routines for discretizing the integrals. We achieve this by directly...

5. Computing higher-order derivatives with AD

Prof. Andrea Walther (Humboldt-Universität zu Berlin)

12/8/25, 2:50 PM

The computation of higher-order derivatives using algorithmic differentiation has been an active area of research for several decades. In recent years, this topic has gained renewed attention due to the growing success of physics-informed neural networks (PINNs), in which such derivatives must be efficiently propagated through corresponding networks.

In this talk, we discuss several...

6. How Automatic Differentiation can help Particle Physics

Jonas Rembser (CERN)

12/9/25, 9:00 AM

Experimentation at high-energy particle colliders is a well-established research field with a clear methodology and plans for large-scale experiments in the future. Its software stack includes packages developed over decades, specialized in particle collision simulation, detector simulation, data aggregation, and statistical analysis. To ensure the software's scalability for future collider...

8. Efficient Differentiable Simulation of Light

Prof. Wenzel Jakob (EPFL)

12/9/25, 9:35 AM

Rendering algorithms simulate light by evaluating high-dimensional integrals that convert 3D scene descriptions into realistic images. Differentiable rendering turns this around: given one or more input observations (photographs or other measurements), it searches for a physical scene model that explains how these images were formed. This view casts many imaging tasks as inverse problems and...